ArticleScientific reports2023
Assessment of gait and posture characteristics using a smartphone wearable system for persons with osteoporosis with and without falls.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 15 citations in OpenAlex.
- The crossroads between osteosarcopenia and intrinsic capacity-a narrative review.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Review
- A Neurosurgeon's Guide to Mobile Health Application Development With a Case Study for Cervical Myelopathy.Neurosurgery · 2026Article
- Measuring Walking Stability with a Mobile Phone in Older Adults: A Validation Study.Sensors (Basel, Switzerland) · 2026Article
- Advances in biomonitoring technologies for women's health.Nature communications · 2025Review
- Review
- Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion.Diagnostics (Basel, Switzerland) · 2024Article
- Gait Assessment Using Smartphone Applications in Older Adults: A Scoping Review.Geriatrics (Basel, Switzerland) · 2024Article
- Enhancing Behavioural Changes: A Narrative Review on the Effectiveness of a Multifactorial APP-Based Intervention Integrating Physical Activity.International journal of environmental research and public health · 2024Review
- Correlation enhanced distribution adaptation for prediction of fall risk.Scientific reports · 2024Article
- Estimating the mechanical cost of transport in human walking with a simple kinematic data-driven mechanical model.PloS one · 2024Article
- Assessing fall risk in osteoporosis patients: a comparative study of age-matched fallers and nonfallers.Frontiers in digital health · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We used smartphone technology to differentiate the gait characteristics of older adults with osteoporosis with falls from those without falls. We assessed gait mannerism and obtained activities of daily living (ADLs) with wearable sensor systems (smartphones and inertial measurement units [IMUs]) to identify fall-risk characteristics. We recruited 49 persons with osteoporosis: 14 who had a fall within a year before recruitment and 35 without falls. IMU sensor signals were sampled at 50 Hz using a customized smartphone app (Lockhart Monitor) attached at the pelvic region. Longitudinal data was collected using MoveMonitor+ (DynaPort) IMU over three consecutive days. Given the close association between serum calcium, albumin, PTH, Vitamin D, and musculoskeletal health, we compared these markers in individuals with history of falls as compared to nonfallers. For the biochemical parameters fall group had significantly lower calcium (P = 0.01*) and albumin (P = 0.05*) and higher parathyroid hormone levels (P = 0.002**) than nonfall group. In addition, persons with falls had higher sway area (P = 0.031*), lower dynamic stability (P < 0.001***), gait velocity (P = 0.012*), and were less able to perform ADLs (P = 0.002**). Thus, persons with osteoporosis with a history of falls can be differentiated by using dynamic real-time measurements that can be easily captured by a smartphone app, thus avoiding traditional postural sway and gait measures that require individuals to be tested in a laboratory setting.
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